Interpretable instance disease prediction based on causal feature selection and effect analysis.

ABSTRACT:

Background

In the big wave of artificial intelligence sweeping the world, machine learning has made great achievements in healthcare in the past few years, however, these methods are only based on correlation, not causation. The particularities of the healthcare determines that the research method must comply with the causality norm, otherwise the wrong intervention measures may bring the patients a lifetime of misfortune.

Methods

We propose a two-stage prediction method (instance feature selection prediction and causal effect analysis) for instance disease prediction. Feature selection is based on the counterfactual and uses the reinforcement learning framework to design an interpretable qualitative instance feature selection prediction. The model is composed of three neura

SUBMITTER: Chen Y 

PROVIDER: S-EPMC8881866 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature